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The single development environment for the entire data science workflow. 

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    Natively analyze your data with a reduction in context switching between services

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    Data to training at scale. Build and train models 5X faster, compared to traditional notebooks.

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    Improve model survivability, with easy connectivity to MLOps services

Benefits

Easy exploration and analysis

Simplified access to data and in-notebook access to machine learning with BigQuery, Dataproc, Spark, and Vertex AI integration.

Rapid prototyping and model development

Take advantage of the power of infinite compute with Vertex AI training for experimentation and prototyping, to go from data to training at scale.

End-to-end notebook workflows

Using Vertex AI Workbench you can implement your continuous integration, training, and deployment workflows from one place.

Key features

Key features

Fully managed compute

A Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities.

MLOps

Vertex AI Workbench will facilitate continuous training, integration and deployment, enabling MLOps from the outset. 

Unified data and ML experience

Seamless visual and code-based integrations with analytics and Vertex AI services.

Documentation

Documentation

Google Cloud Basics
Vertex AI Workbench documentation

Learn more about Vertex AI Workbench. 

Google Cloud Basics
Vertex AI documentation

Explore Vertex AI product documentation, from introductory to advanced.

Google Cloud Basics
Explore end-to-end ML on Vertex AI Workbench in this Codelab

In this lab, you'll learn how to use Vertex Workbench for data exploration and ML model training.

Google Cloud Basics
Overview of Vertex AI Workbench at Google Cloud Next 2021

Watch "Vertex AI is now even easier for developers" from Google Cloud Next 2021 to explore how Vertex AI Workbench is helping customers to deploy more useful models.

All features

All features

Simplified data access Extensions will seamlessly connect to the entire data estate including BigQuery, Data Lake, Dataproc, and Spark. Seamlessly scale up or scale out depending on your analytic and AI needs.
Explore data sources using a catalog Write SQL, Spark queries from a syntax-aware, auto-complete enabled notebook cell.
Data visualization Integrated, intelligent visualization tools will provide easy insights into data. 
Hands-off, cost-effective infrastructure All aspects of the compute are manged. Idle timeout and auto shutdown will optimize total cost of ownership.
Enterprise security, simplified Out-of-the-box Google Cloud security controls. Single sign-on and simple authentication to other Google Cloud services.
Data Lake and Spark in one place Whether you use TensorFlow, PyTorch, or Spark, you can run any engine from Vertex AI Workbench. 
Deep Git, training, and MLOps integration With few clicks, plug notebooks into established Ops workflows. Use notebooks for distributed training, hyper-parameter optimization, or scheduled or triggered continuous training. Deep integration with Vertex AI services brings MLOps into the notebook without the need to rewrite code or new workflows.
Seamless CI/CD Kubeflow Pipelines integration to use Notebooks as an ideal, tested, and verified deployment target. 
Notebook viewer Share output of periodically updated notebook cells for reporting and bookkeeping purposes.

Pricing

Pricing

Vertex AI Workbench's pricing details can be found here.

The pricing model is based upon compute and services based on the infrastructure you use, as well as other services consumed from within Vertex AI Workbench.